RISK ANALYSIS METHODS
RISK ANALYSIS METHODS
Risk management can be divided into four steps: risk identification, risk assessment, risk control, and risk records. In recent years, studies have mostly focused on the risk assessment. Risk assessment is to analyze and measure the size of risks in order to provide information to risk control. Four steps are included in the risk assessment.
- According to the results of risk identification and build an appropriate mathematical model.
- through expert surveys, historical records, extrapolation, etc. to obtain the necessary, basic information or data available, and then choose the appropriate mathematical methods to quantify the information.
- Choose proper models and analysis methods to deal with the data and adjust the models according to the specific circumstances.
- Determine the size of risks according to certain criteria. In the risk assessment extrapolation, subjective estimation, probability distribution analysis and other methods are used to obtain some basic data or information. Further data analysis often use following basic theory and methods: layer analysis method, mode cangue logical analysis method, Monte Carlo simulation, the gray system theory, artificial neural network method, fault tree analysis, Bayesian theory, an influence diagram method and Markov process theory.
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We can divide the methods into qualitative analysis and Quantitative Analysis.
1. Fault Tree Analysis
Fault Tree Analysis
Fault Tree Analysis (Fault Tree Analysis, FTA) can be used for qualitative analysis of risk and can also be used for quantitative analysis. It is mainly used for large-scale complicated system reliability and safety analysis. It is also an effective method to Unification reliability and safety analysis, through hardware, software, environment, human factors.FTA is drawing a variety of possibilities of failure in system failure analysis, from whole to part, according to the tree structure. Fault tree analysis using tree form, the system
The failure of components and composition of the fault system are connected. We are always using fault tree in qualitative or quantitative risk analysis. The difference in them is that the quantitative fault tree is good in structure and it requires use of the same rigorous logic as the formal fault tree, but qualitative fault tree is not. Fault tree analysis system is based on the target which event is not hoped to happen (called the top event), one level down from the top event analysis of the direct cause of their own events (call low event), according to the logical relationship between the upper and lower case, the analysis results are obtained.
2. Event Tree Analysis
Event tree analysis (event tree analysis, ETA) also known as decision tree analysis, is another important method of risk analysis. It is the events of a given system, the analysis of the events may cause a series of results, and thus evaluates the possibility of the system. Event tree is given an initial event all possible ways and means of development, every aspect of the event tree events (except the top incidents) are the implementation of certain functions of measures to prevent accidents, and all have binary outcomes (success or failure). While the event tree illustrates the various incidents causes of the accident sequence group. Through various intermediate steps in the accident sequence group can organize the complexity of the relationship between the initial incident and the probability of systemic risk reduction measurement, and identify the accident sequence group. So we can calculate the probability of each of the key sequence of events occurred.
3. Cause-Consequence Analysis
Cause and consequence analysis is a combination of fault tree analysis and event tree analysis. It uses the cause analysis (fault tree analysis) and the result analysis (event tree analysis), CCA aims to identify the chain of events leading to unexpected consequences, according to the probability of occurrence of different events from CCA diagram to calculate the probability of different results, then the risk level of the system can be determined.
4. Preliminary Risk Analysis
Preliminary risk analysis or hazard analysis is a qualitative technique which involves a disciplined analysis of the event sequences which could transform a potential hazard into an accident. In this technique, the possible undesirable events are identified first and then analyzed separately. 2 For each undesirable events or hazards, possible improvements, or preventive measures are then formulated.
This method provides a basis for determining hazard categories and which analysis methods are most suitable. It is proved valuable in the working surrounding to which activities lacking safety measures can be readily identified.
5. Hazard and Operability studies (HAZOP)
The HAZOP technique was origined in the early 1970s by Imperial Chemical Industries Ltd. HAZOP is firstly defined as the application of a formal systematic critical examination of the process and engineering intentions of new or existing facilities to assess the hazard potential that arise from deviation in design specifications and the consequential effects on the facilities as a whole.2
This technique is usually performed using a set of guidewords: NO/NOT, MORE OR/LESS OF, AS WELL AS, PART OF REVERSE, AND OTHER THAN. These guidewords, a scenario that may result in a hazard or an operational problem is identified. Consider the possible flow problems in a process line, the guide word MORE OF will correspond to high flow rate, while that for LESS THAN, low flow rate. The consequences of the hazard and measures to reduce the frequency with which the hazard will occur are then discussed. This technique is accepted widely in the process industries. It is mostly regarded as an effective tool for plant safety and operability improvements. Detailed procedures on how to perform the technique are available in some relevant literatures.
Fault Tree Analysis
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It is explained in the Qualitative analysis.
Expected value is the possible outcome times the probability of its occurrence. An expected value shows the percentage of yielding a target in a business.
In sensitivity analysis shows how the outcome changes in response of a particular variable change. One can get result from optimistic, most likely and pessimistic values. An example of inputs for sensitivity analysis is the material and labor cost that can be much fluctuated.
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